多种充电模式协同的规模化电动汽车分层充电方法

doi:10.16183/j.cnki.jsjtu.2023.564

上海交通大学学报 ›› 2025, Vol. 59 ›› Issue (9): 1304-1314.doi: 10.16183/j.cnki.jsjtu.2023.564

• 新型电力系统与综合能源 • 上一篇下一篇

多种充电模式协同的规模化电动汽车分层充电方法

刘永江¹, 郭杉², 贾俊青², 刘小恺², 蔡文超², 曾龙³()

¹ 内蒙古电力(集团)有限责任公司, 呼和浩特 010010
² 内蒙古电力科学研究院, 呼和浩特 010020
³ 上海交通大学电子信息与电气工程学院, 上海 200240

收稿日期:2023-11-10 修回日期:2024-01-06 接受日期:2024-01-10 出版日期:2025-09-28 发布日期:2025-09-25
通讯作者: 曾龙,博士后;E-mail:zenglong2404@qq.com.
作者简介:刘永江(1977—),正高级工程师,从事综合能源系统优化调度研究.
基金资助:
内蒙古自治区“揭榜挂帅”项目(2022JBGS0043)

Electric Vehicles Hierarchical Charging Method Considering Multiple Modes Coordination

LIU Yongjiang¹, GUO Shan², JIA Junqing², LIU Xiaokai², CAI Wenchao², ZENG Long³()

¹ Inner Mongolia Power (Group) Co., Ltd., Hohhot 010010, China
² Inner Mongolia Power Science Research Institute, Hohhot 010020, China
³ School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

Received:2023-11-10 Revised:2024-01-06 Accepted:2024-01-10 Online:2025-09-28 Published:2025-09-25

摘要/Abstract

摘要：

针对规模化电动汽车(EV)随机充电负荷冲击电网的问题,提出了一种多充电模式协同的规模化EV分层充电方法.通过利用不同充电方式特点,避免充电负荷集中于电力负荷高峰时间段.针对内蒙古某地区电力负荷曲线,考虑当地可再生能源丰富特点,基于K-means和肘部法则聚类成当地5类典型电力负荷曲线.考虑EV通过充电桩充电和换电站换电池,分别建立充电站充电模型和换电站电池交换模型,同时建立规模化EV分层能量交换模型.在上层中,根据用户特征以及充电方式成本,基于粒子群算法对EV与充电方式进行匹配;在下层中,考虑电力价格、用户需求、充电站运行情况和快、慢充特点,利用MATLAB自带优化工具箱优化充电站内EV、电池充电优化方案.实验仿真表明,本文所提方法采取的不同充电方式协同可实现充电方式优势互补,有效避免了充电负荷集中于电力负荷高峰时间段,可持续、经济地满足EV用户充电需求;但是单以电力价格作为EV充电负荷的引导信号,有可能加剧当地电力负荷曲线峰谷差和扰动情况.

关键词: 电动汽车, 充电桩, 电池交换, 充电模式协同, 分层优化

Abstract:

To alleviate the adverse effect of large-scale electric vehicles (EVs) random charging, an EV hierarchical charging method considering multiple modes coordination is proposed in this paper, which avoids the large-scale charging load centralized in a certain period by coordinating diverse charging modes. Considering the load characteristics such as the output of renewable power generation, the power load curves of an area in Inner Mongolia Autonomous Region are clustered into five typical power load curves based on the K-means clustering algorithm and elbow method. According to the characteristics of EV charging and battery swapping (BS) modes, the charging station models and EV hierarchical power exchange model are established. At the upper level, the users’ requirements and charging costs are considered, and EVs are matched with the charging mode based on the particle swarm optimization algorithm. At the lower level, the electric price and charging station operation conditions are considered, and the charging schemes in the charging/BS station are optimized based on the optimization toolbox in the MATLAB software platform. Extensive case studies are conducted to validate the effectiveness of the proposed method, where a large number of EVs charge continuously with cost efficiency. However, relying solely on electricity price as the control signal for EV charging load may exacerbate the valley-to-peak disparity and instability of the local power load curve.

Key words: electric vehicle (EV), electric vehicle charger, battery swapping (BS), charging modes coordination, hierarchical optimization

中图分类号:

TM732

刘永江, 郭杉, 贾俊青, 刘小恺, 蔡文超, 曾龙. 多种充电模式协同的规模化电动汽车分层充电方法[J]. 上海交通大学学报, 2025, 59(9): 1304-1314.

LIU Yongjiang, GUO Shan, JIA Junqing, LIU Xiaokai, CAI Wenchao, ZENG Long. Electric Vehicles Hierarchical Charging Method Considering Multiple Modes Coordination[J]. Journal of Shanghai Jiao Tong University, 2025, 59(9): 1304-1314.

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参考文献 23

[1]	李高俊杰, 杨军, 朱旭, 等. 计及电动汽车用户响应特性的充电站实时电能共享机制[J]. 电力系统自动化, 2022, 46(12): 56-66.
	LI Gaojunjie, YANG Jun, ZHU Xu, et al. Real-time energy sharing mechanism of charging stations considering user response characteristics of electric vehicles[J]. Automation of Electric Power Systems, 2022, 46(12): 56-66.
[2]	CAO Y, ZHOU B, CHUNG C Y, et al. Dynamic modelling and mutual coordination of electricity and watershed networks for spatio-temporal operational flexibility enhancement under rainy climates[J]. IEEE Transactions on Smart Grid, 2023, 14(5): 3450-3464.
[3]	LIU X, HAN W, LIU Y, et al. A coordinated voltage-frequency support method for VSC-MTDC integrated offshore wind farms system[J]. IEEE Transactions on Power Systems, 2024, 39(1): 1485-1502.
[4]	ZENG L, CHEN S, ZHONG C, et al. Hierarchical transactive power exchange method on expressways for EVs energy supplement[J]. Frontiers in Energy Research, 2023, 11: 1213883.
[5]	ZENG L, LI C, LI Z, et al. Hierarchical dispatching method based on Hungarian algorithm for reducing the battery degradation cost of EVs participating in frequency regulation[J]. IET Generation, Transmission and Distribution, 2020, 14(23): 5617-5625.
[6]	吴翠玉, 张美霞, 陈海燕, 等. 国内外电动汽车扶持政策比较与分析[J]. 上海电力学院学报, 2016, 32(2): 188-192.
	WU Cuiyu, ZHANG Meixia, CHEN Haiyan, et al. Comparative analysis of supportive policies for electric vehicles at home and abroad[J]. Journal of Shanghai University of Electric Power, 2016, 32(2): 188-192.
[7]	蒋菱, 王旭东, 庄剑, 等. 智能电网高适应性发展建设模式与政策建议[J]. 电力系统及其自动化学报, 2015, 27(Sup.1): 120-125.
	JIANG Ling, WANG Xudong, ZHUANG Jian, et al. Policy recommendations & development construction mode of high adaptability of smart grid[J]. Proceedings of the CSU-EPSA, 2015, 27(Sup.1): 120-125.
[8]	杨天宇, 郭庆来, 盛裕杰, 等. 系统互联视角下的城域电力-交通融合网络协同[J]. 电力系统自动化, 2020, 44(11): 1-9.
	YANG Tianyu, GUO Qinglai, SHENG Yujie, et al. Coordination of urban integrated electric power and traffic network from perspective of system interconnection[J]. Automation of Electric Power Systems, 2020, 44(11): 1-9.
[9]	ZHOU T, SUN W. Research on multi-objective optimisation coordination for large-scale V2G[J]. IET Renewable Power Generation, 2020, 14(3): 445-453.
[10]	王岩庆, 王骁, 丛若晨, 等. 考虑配电网运行安全的出行电动汽车充电引导策略[J]. 高电压技术, 2023, 49(5): 2131-2139.
	WANG Yanqing, WANG Xiao, CONG Ruochen, et al. Charging guidance strategy of traveling electric vehicle considering the operation safety of distribution network[J]. High Voltage Engineering, 2023, 49(5): 2131-2139.
[11]	朱磊, 黄河, 高松. 计及风电消纳的电动汽车负荷优化配置研究[J]. 中国电机工程学报, 2021, 41(Sup.1): 194-203.
	ZHU Lei, HUANG He, GAO Song, et al. Research on optimal load allocation of electric vehicle considering wind power consumption[J]. Proceeding of the CSEE, 2021, 41(Sup.1): 194-203.
[12]	姚丽娟, 蔡瑞天, 钱江, 等. 面向低碳园区供需平衡的混杂负荷系统聚合和控制模型构建[J]. 电网技术, 2023, 47(8): 3153-3166.
	YAO Lijuan, CAI Ruitian, QIAN Jiang, et al. Aggregation and control model construction of hybrid load system for supply and demand balance in low-carbon zone[J]. Power System Technology, 2023, 47(8): 3153-3166.
[13]	贾士铎, 康小宁, 黑皓杰, 等. 基于V2G负荷反馈修正的电热氢综合能源系统多层协调优化调度[J]. 电力系统自动化, 2023, 47(15): 100-110.
	JIA Shiduo, KANG Xiaoning, HEI Haojie, et al. Multi-layer coordinated optimal dispatch of electric-thermal-hydrogen integrated energy system based on V2G load feedback correction[J]. Automation of Electric Power Systems, 2023, 47(15): 100-110.
[14]	JOZI F, ABDALI A, MAZLUMI K, et al. Reliability improvement of the smart distribution grid incorporating EVs and BESS via optimal charging and discharging process scheduling[J]. Frontiers in Energy Research, 2022, 10: 920343.
[15]	LI D, ZHANG L, ZHANG Z, et al. Battery safety issue detection in real-world electric vehicles by integrated modeling and voltage abnormality[J]. Energy, 2023, 284: 128438.
[16]	孙欣, 江海林, 谢敬东, 等. 基于用户信用指数的园区电动汽车充放电调度机[J]. 上海交通大学学报, 2025, 59(2): 200-207. doi: 10.16183/j.cnki.jsjtu.2023.196
	SUN Xin, JIANG Hailin, XIE Jingdong, et al. Aggregation and control model construction of hybrid load system for supply and demand balance in low-carbon zone[J]. Journal of Shanghai Jiao Tong University, 2025, 59(2): 200-207.
[17]	GUO D, ZHOU C. Potential performance analysis and future trend prediction of electric vehicle with V2G/V2H/V2B capability[J]. AIMS Energy, 2016, 4(2): 331-346.
[18]	BORGE-DIEZ D, ICAZA D, ACIKKALP E, et al. Combined vehicle to building (V2B) and vehicle to home (V2H) strategy to increase electric vehicle market share[J]. Energy, 2021, 237: 121608.
[19]	LI S, ZHAO P, GU C, et al. Aging mitigation for battery energy storage system in electric vehicles[J]. IEEE Transactions on Smart Grid, 2023, 13(3): 2152-2163.
[20]	崔杨, 李翼成, 付小标, 等. 基于换电服务定价策略及动态调控方法的含充换电站微电网系统双层优化调度[J]. 电网技术, 2023, 47(5): 1998-2011.
	CUI Yang, LI Yicheng, FU Xiaobiao, et al. Double-layer optimal scheduling of micro-grid system with charging and swapping stations based on battery swap service pricing strategy and dynamic regulation[J]. Power System Technology, 2023, 47(5): 1998-2011.
[21]	ZENG L, LI C, LI Z, et al. Hierarchical bipartite graph matching method for transactive V2V power exchange in distribution power system[J]. IEEE Transactions on Smart Grid, 2021, 12(1): 301-311.
[22]	ZENG L, CHEN S, TANG Z, et al. An electric vehicle charging method considering multiple power exchange modes’ coordination[J]. Sustainability, 2023, 15: 10520.
[23]	DATTA U, SAIPRASAD N, KALAM A, et al. A price-regulated electric vehicle charge-discharge strategy for G2V, V2H, and V2G[J]. International Journal of Energy Research. 2019, 43: 1032-1042.

参数	取值	参数	取值
T/h	24	P_max/kW	70
Δt/h	1	P_min/kW	0
t₀/h	0	${F}_{n}^{ini}$	0.4
N_EV	100	${F}_{n}^{obj}$	0.8
M_B	100	Q_n/(kW·h)	70
α₁/(美元·kW⁵)	2.281×10^-7	F_max	0.8
α₂/(美元·kW²)	8.442×10^-7	F_min	0.2
β₁	5	${F}_{m}^{obj}$	0.8
β₂	2	${F}_{m}^{ini}$	0.4

方法	电力成本/美元	电池损耗成本/美元	总成本/美元
方法1	2 514.8	655.0	3 169.9
方法2	2 418.6	221.7	2 640.3
所提方法	2 260.5	106.9	2 367.4

EV	停车初始时刻	停车时长/h
1	7:00	1
2	12:00	2
3	19:00	4

EV	方法	电力成本/ 美元	电池损耗成本/美元	总成本/ 美元
1	方法1	2.125 2	3.927 0	6.052 2
	方法2	1.548 5	0.026 7	1.575 2
	所提方法	1.548 5	0.026 7	1.575 2
2	方法1	3.215 0	0.264 9	3.479 9
	方法2	3.744 4	0.245 8	3.990 2
	所提方法	3.215 0	0.264 9	3.479 9
3	方法1	3.249 2	0.078 0	3.327 2
	方法2	3.570 0	3.926 3	7.496 3
	所提方法	3.198 1	0.109 3	3.307 4

方法	峰谷差/ MW	平均负荷/ MW	方差/ kW²	风光出力消纳/MW
无叠加	0.008 4	-0.001 9	2.600	0
方法1	1.571 6	0.942 0	477.1	0.005
方法2	1.442 2	0.942 0	437.0	0.004
所提方法	1.866 3	0.942 0	513.6	0.004

多种充电模式协同的规模化电动汽车分层充电方法

Electric Vehicles Hierarchical Charging Method Considering Multiple Modes Coordination

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参考文献 23

相关文章 15

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方法	峰谷差/ MW	平均负荷/ MW	方差/ kW²
无叠加	0.067 8	0.056 5	0.018 3
方法1	1.601 0	1.000 4	0.487 4
方法2	1.480 7	1.000 4	0.448 4
所提方法	1.889 1	1.000 4	0.521 4

方法	峰谷差/ MW	平均负荷/ MW	方差/ kW²	风光出力消纳/MW
无叠加	0.181 0	-0.043 7	63.50	0
方法1	1.611 5	0.900 1	531.6	0.873 4
方法2	1.690 1	0.900 1	492.9	0.979 7
所提方法	2.000 3	0.900 1	550.5	0.979 7

方法	峰谷差/ MW	平均负荷/ MW	方差/ kW²
无叠加	0.119 3	0.046 2	40.40
方法1	1.587 7	0.990 1	461.0
方法2	1.377 2	0.990 1	415.1
所提方法	1.833 3	0.990 1	508.2

方法	峰谷差/ MW	平均负荷/ MW	方差/ kW²	风光出力消纳/MW
无叠加	0.096 9	-0.015 7	32.80	0
方法1	1.476 7	0.928 2	450.8	0.574 8
方法2	1.374 9	0.928 2	411.8	0.574 8
所提方法	1.817 3	0.928 2	494.0	0.574 8

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[4]	. 基于多种影响因素的电动汽车充电负荷建模方法[J]. J Shanghai Jiaotong Univ Sci, 2025, 30(6): 1232-1241.
[5]	孙欣, 江海林, 谢敬东, 王思敏, 王森. 基于用户信用指数的园区电动汽车充放电调度机制[J]. 上海交通大学学报, 2025, 59(2): 200-207.
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